hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
# which(hilo$date=="2010-10-23 00:00:00") = index 529
# which(hilo$date=="2016-12-31 23:00:00") = index 54816
length(hilo[,1]) # we are left with 54288 lines of data
<<<<<<< Updated upstream
[1] 54288
54816 - length(hilo[,1]) # we lost 528 values
=======
[1] 54288
54816 - length(hilo[,1]) # we lost 528 values
>>>>>>> Stashed changes
[1] 528
# removing columns that we are not using
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen
# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???
hilo
<<<<<<< Updated upstream
====================================================
length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs
[1] 12
which(is.na(hilo$logcms)==TRUE)
[1] 50509 50510 50511 50512 50513 50514 50515 50516 50517 50518
[11] 50519 50520
RiverFlow <- ggplot(hilo, aes(x = date, y = as.numeric(cms))) +
geom_line()
print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs
[1] 20464
which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max
[1] 38974
# CHL tells us where in the data set this happened
hilo[38974,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
CHL <- ggplot(hilo, aes(x = date, y = as.numeric(ChlRFU))) +
geom_line()
print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll - relative fluorescence units (RFU)"))
<<<<<<< Updated upstream
length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs
[1] 15012
which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4
[1] 33243
# tells us where in the data set this happened
hilo[33243,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) +
geom_line()
print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
<<<<<<< Updated upstream
length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs
[1] 11330
SALT <- ggplot(hilo, aes(x = date, y = as.numeric(saltppt))) +
geom_line()
print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
<<<<<<< Updated upstream
======================================================== # Histograms FULL DATA SET ## River Flow Histogram
hist(as.numeric(hilo$logcms), main = "Histogram of Log River Flow", xlab = "Log River Flow")
<<<<<<< Updated upstream
# this looks okay
# VERY skewed
hist(as.numeric(hilo$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll - relative fluorescence units (RFU)")
# this looks better
hist(log(as.numeric(hilo$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
NaNs produced
# not sure what happens to units when taking the log
# VERY skewed
hist(as.numeric(hilo$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")
# this looks better
hist(log(as.numeric(hilo$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log
# skewed
hist(as.numeric(hilo$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")
# this is worst!
hist(log(as.numeric(hilo$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log
========================================================= # NEW DATA SET-Modified Data 2013-2015
# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])
[1] 28008
# lost 28008 entries of data
length(hilomodified[,1])-528 # we are left with 25752 entries of data
[1] 25752
head(hilomodified)
<<<<<<< Updated upstream
tail(hilomodified)
<<<<<<< Updated upstream
NA
library(mosaic)
<<<<<<< Updated upstream
Auto-converting character to numeric.
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'mosaic':
method from
fortify.SpatialPolygonsDataFrame ggplot2
The 'mosaic' package masks several functions from core packages in order to add
additional features. The original behavior of these functions should not be affected by this.
Attaching package: ‘mosaic’
The following object is masked from ‘package:Matrix’:
mean
The following object is masked from ‘package:scales’:
rescale
The following objects are masked from ‘package:dplyr’:
count, do, tally
The following object is masked from ‘package:ggplot2’:
stat
The following objects are masked from ‘package:stats’:
binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test, quantile, sd, t.test,
var
The following objects are masked from ‘package:base’:
max, mean, min, prod, range, sample, sum
>>>>>>> Stashed changes
favstats(hilomodified$ChlRFU)
Auto-converting character to numeric.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
favstats(hilomodified$Corr.NTU)
Auto-converting character to numeric.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
favstats(hilomodified$saltppt)
Auto-converting character to numeric.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
favstats(hilomodified$TempC)
Auto-converting character to numeric.
favstats(hilomodified$Doper)
Auto-converting character to numeric.
===================================================== # MODIFIED DATA SET 2013-2015 # Descriptives: Plots
length(hilomodified$logcms[which(is.na(hilomodified$logcms)==TRUE)]) # No NAs, YAY!
[1] 0
which(is.na(hilomodified$logcms)==TRUE)
integer(0)
RiverFlowMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(cms))) +
geom_line()
print(RiverFlowMod + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
sum(is.na(hilomodified$ChlRFU)==TRUE) # 1884 NAs
[1] 1884
#which(is.na(hilomodified$ChlRFU)==TRUE)
# max(as.numeric(na.omit(hilo$ChlRFU))) # 15.3
which(as.numeric(hilomodified$ChlRFU)==15.3)
[1] 19750
hilo[38974,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
CHLMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(ChlRFU))) +
geom_line()
print(CHLMod + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll - relative fluorescence units (RFU)"))
<<<<<<< Updated upstream
length(hilomodified$Corr.NTU[which(is.na(hilomodified$Corr.NTU)==TRUE)]) # 2704 NAs
[1] 2704
# max(as.numeric(na.omit(hilo$Corr.NTU))) # 88.4
which(as.numeric(hilo$Corr.NTU)==88.4)
[1] 33243
hilo[33243,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
TURBMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(Corr.NTU))) +
geom_line()
print(TURBMod + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
<<<<<<< Updated upstream
length(hilomodified$saltppt[which(is.na(hilomodified$saltppt)==TRUE)]) # 2267 NAs
[1] 2267
SALTMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(saltppt))) +
geom_line()
print(SALTMod + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
length(hilomodified$TempC[which(is.na(hilomodified$TempC)==TRUE)]) # 2267 NAs
[1] 1702
TempMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(TempC))) +
geom_line()
print(TempMod + ggtitle("Temperature")+labs(x="Time", y = "Temperature - Celsius"))
length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs
TempMod <- ggplot(hilomodified, aes(x = date, y = as.numeric(Doper))) +
geom_line()
print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))
=================================================== # Histograms MODIFIED
hist(as.numeric(hilomodified$logcms), main = "Histogram of Log River Flow", xlab = "Log River Flow")
hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =50, xlim = c(0,200))
# this looks okay
# VERY skewed
hist(as.numeric(hilomodified$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll - relative fluorescence units (RFU)")
<<<<<<< Updated upstream
# this looks better
hist(log(as.numeric(hilomodified$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
NaNs produced
<<<<<<< Updated upstream
# not sure what happens to units when taking the log
# VERY skewed
hist(as.numeric(hilomodified$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")
<<<<<<< Updated upstream
# this looks better
hist(log(as.numeric(hilomodified$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
<<<<<<< Updated upstream
# not sure what happens to units when taking the log
# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")
# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log
=============================================================
It is hard to see what is going on
AllYears <- ggplot(hilomodified, aes(x = date, y = as.numeric(logcms))) +
geom_line()+
geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
print(AllYears + ggtitle("Hilo Bay")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
============================================================== # Descriptives by Storm We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. We will not use the log (which is logbase10) in order to see the extreme events When salinity is below 35 this also indicates a storm has occurred.
We will break the data set by year to find the most extreme event for each year.
hilo2013 <- hilomodified[1:8760,]
hilo2014 <- hilomodified[8761:17520,]
hilo2015 <- hilomodified[17521:length(hilomodified[,1]),]
# max(as.numeric(hilo2013$logcms)) # this is log base 10
# This is NOT the natural log
max(as.numeric(hilo2013$cms)) # 207.186
[1] 207.186
which(as.numeric(hilo2013$cms) == max(as.numeric(hilo2013$cms)))
[1] 3550
hilo2013[3550,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
# all values for this storm are NA
plot2013 <- ggplot(hilo2013, aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
print(plot2013 + ggtitle("2013")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
# R2013.1 <- ggplot(hilo2013[1:(length(hilo2013[,1])/2),], aes(x = date, y = as.numeric(cms))) +
# geom_line(color="black")+
# geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
# geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
# geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.1 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.1+ylim(0,40)
#
#
# R2013.2 <- ggplot(hilo2013[(length(hilo2013[,1])/2):length(hilo2013[,1]),], aes(x = date, y = as.numeric(cms))) +
# geom_line(color="black")+
# geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
# geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
# geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.2 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.2+ylim(0,40)
# Max river flow in the overall data set
max(as.numeric(hilo2014$cms))
[1] 472.4939
max(as.numeric(hilomodified$cms))
[1] 472.4939
which(as.numeric(hilo2014$cms) == max(as.numeric(hilo2014$cms)))
[1] 5263
hilo2014[5263,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
R2014 <- ggplot(hilo2014, aes(x = date, y = as.numeric(logcms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
print(R2014 + ggtitle("2014")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
max(as.numeric(hilo2015$cms))
[1] 232.2449
which(as.numeric(hilo2015$cms) == max(as.numeric(hilo2015$cms)))
[1] 6475
hilo2015[6475,]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
R2015 <- ggplot(hilo2015, aes(x = date, y = as.numeric(logcms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
print(R2015 + ggtitle("2015")+labs(x="Time", y = "River Flow - cubic meters per second"))
<<<<<<< Updated upstream
outcomes <- as.numeric(hilomodified$saltppt)<25
index.lt25 <- which(outcomes==TRUE)
poss.storms <- hilomodified[index.lt25,]
poss.storms
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
outcomes.35 <- as.numeric(hilomodified$saltppt)<35
index.lt35 <- which(outcomes==TRUE)
index.lt25
<<<<<<< Updated upstream
[1] 159 160 161 162 163 164 209 210 213 218
[11] 225 227 228 230 231 232 234 759 765 766
[21] 815 816 894 895 899 900 901 902 903 904
[31] 905 906 907 908 1112 1113 1114 1115 1116 1117
[41] 1118 1119 1120 1121 1122 1123 1124 1125 1126 1131
[51] 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141
[61] 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151
[71] 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
[81] 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171
[91] 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181
[101] 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191
[111] 1192 1193 1194 1195 1196 1197 1198 1200 1201 1202
[121] 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213
[131] 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223
[141] 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233
[151] 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243
[161] 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253
[171] 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263
[181] 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273
[191] 1274 1275 1276 1277 1278 1279 1283 1284 1285 1286
[201] 1288 1289 1293 1294 1295 1296 1297 1298 1299 1300
[211] 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310
[221] 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320
[231] 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330
[241] 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340
[251] 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350
[261] 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
[271] 1361 1362 1363 1364 1365 1366 1368 1369 1370 1371
[281] 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381
[291] 1382 1383 1384 1391 1392 1393 1410 1411 1413 1414
[301] 1415 1416 1417 1418 1419 1420 1421 1424 1425 1427
[311] 1428 1431 1432 1433 1434 1435 1436 1437 1438 1439
[321] 1440 1442 1443 1444 1445 1446 1447 1450 1451 1452
[331] 1453 1454 1458 1478 1479 1480 1481 1482 1483 1484
[341] 1490 1515 2137 3485 3487 3488 3489 3490 3491 3492
[351] 3495 3496 3497 3498 3499 3500 3501 3502 3503 3508
[361] 3509 3513 3518 3519 5312 5313 5317 5318 5319 5320
[371] 5321 5322 5323 5325 5326 5327 5328 5329 5330 5331
[381] 5332 5334 5335 5336 5337 5338 5339 5340 5341 5342
[391] 5343 5344 5345 5346 5347 5348 5349 5350 5368 5474
[401] 5475 5485 5486 5487 5488 5489 5491 5492 5509 5756
[411] 7545 8672 8673 8674 8675 8676 8677 8678 8679 8680
[421] 8681 8682 8683 8713 8714 8715 8716 8717 8718 8719
[431] 8720 8721 8722 8723 8724 8725 8726 8727 8728 8729
[441] 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739
[451] 8740 8741 8742 8743 8744 8745 8746 8747 8748 8749
[461] 8750 8751 8752 8753 8754 8755 8756 8757 8758 8759
[471] 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769
[481] 8770 8771 8772 8773 8774 8775 8776 8835 8836 8837
[491] 8839 8840 8841 8842 8843 8844 8845 8846 8847 8848
[501] 8849 8855 8856 8857 8858 8859 8862 8863 8864 8865
[511] 8876 8927 8932 8933 8945 10394 10395 10396 10397 10398
[521] 10399 10400 10401 10402 10403 10404 10405 10406 10407 10408
[531] 10410 10411 10413 10414 10421 10422 10425 10427 10428 10429
[541] 10430 10431 10432 10433 10434 10435 10436 10437 10438 10441
[551] 10442 10443 10444 10445 10446 10450 10538 10539 10540 10541
[561] 10544 10545 10546 10547 10552 10553 10554 10555 10556 10557
[571] 10558 10559 10570 10644 10647 10652 10656 10657 10659 10664
[581] 10665 10666 10667 10668 10669 10670 10671 10672 10673 10674
[591] 10675 10676 10677 10678 10679 10680 10681 10682 10683 10684
[601] 10685 10686 10687 10688 10689 10690 10691 10692 10693 10694
[611] 10695 10696 10697 10698 10699 10700 10701 10702 10703 10704
[621] 10705 10706 10707 10708 10709 10710 10711 10712 10713 10714
[631] 10715 10716 10717 10718 10719 10720 10721 10722 10723 10724
[641] 10725 10726 10727 10728 10729 10730 10731 10732 10733 10734
[651] 10735 10736 10737 10738 10739 10740 10741 10742 10743 10744
[661] 10745 10746 10747 10748 10749 10750 10751 10752 10753 10754
[671] 10755 10756 10757 10758 10759 10760 10761 10762 10763 10764
[681] 10765 10766 10767 10768 10769 10770 10771 10772 10773 10774
[691] 10775 10776 10777 10778 10779 10780 10781 10782 10783 10784
[701] 10785 10786 10787 10788 10789 10790 10791 10792 10793 10794
[711] 10795 10796 10797 10798 10799 10800 10801 10802 10803 10804
[721] 10805 10806 10807 10808 10809 10810 10811 10812 10813 10814
[731] 10815 10816 10817 10818 10819 10820 10830 10831 10832 10833
[741] 10919 10920 10921 10922 10923 10924 10925 10926 10927 10928
[751] 10929 10930 10931 10932 10933 10934 10935 10936 10937 10938
[761] 10939 10940 10941 10942 10943 10944 10945 10946 10947 10948
[771] 10949 10950 10951 10952 10953 10954 10955 10956 10957 10958
[781] 10959 10960 10964 10965 10966 10967 10968 10969 10970 10971
[791] 10972 10973 10974 10975 10976 10977 10978 10979 10980 10981
[801] 10982 10983 10984 10985 10986 10987 10988 10989 10990 10991
[811] 10992 10993 10994 10995 10997 10998 11000 11001 11002 11003
[821] 11004 11005 11006 11007 11008 11009 11010 11011 11012 11019
[831] 11020 11021 11022 11023 11024 11025 11026 11027 11028 11030
[841] 11031 11032 11033 11065 11077 11086 11093 11094 11095 11096
[851] 11097 11098 11099 11100 11101 11102 11103 11104 11105 11106
[861] 11107 11108 11109 11110 11111 11112 11113 11114 11115 11116
[871] 11117 11118 11119 11120 11122 11123 11124 11125 11126 11127
[881] 11128 11129 11130 11131 11132 11133 11134 11135 11136 11139
[891] 11140 11149 11150 11151 11204 11205 11210 11211 11212 11213
[901] 11214 11215 11216 11217 11218 11219 11220 11221 11222 11223
[911] 11224 11225 11226 11227 11228 11229 11230 11231 11232 11233
[921] 11234 11235 11236 11237 11238 11239 11240 11241 11242 11243
[931] 11244 11245 11246 11247 11248 11249 11250 11251 11252 11253
[941] 11254 11255 11256 11257 11258 11259 11260 11261 11262 11263
[951] 11264 11265 11266 11267 11268 11269 11270 11271 11272 11273
[961] 11274 11275 11276 11277 11278 11279 11280 11281 11282 11283
[971] 11284 11285 11286 11287 11288 11289 11290 11291 11292 11293
[981] 11294 11295 11296 11297 11298 11299 11300 11301 11302 11303
[991] 11304 11305 11306 11307 11308 11309 11310 11311 11312 11313
=======
[1] 159 160 161 162 163 164 209 210 213 218 225 227
[13] 228 230 231 232 234 759 765 766 815 816 894 895
[25] 899 900 901 902 903 904 905 906 907 908 1112 1113
[37] 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125
[49] 1126 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141
[61] 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153
[73] 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165
[85] 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177
[97] 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
[109] 1190 1191 1192 1193 1194 1195 1196 1197 1198 1200 1201 1202
[121] 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215
[133] 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227
[145] 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239
[157] 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251
[169] 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263
[181] 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275
[193] 1276 1277 1278 1279 1283 1284 1285 1286 1288 1289 1293 1294
[205] 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306
[217] 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318
[229] 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330
[241] 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342
[253] 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354
[265] 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366
[277] 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379
[289] 1380 1381 1382 1383 1384 1391 1392 1393 1410 1411 1413 1414
[301] 1415 1416 1417 1418 1419 1420 1421 1424 1425 1427 1428 1431
[313] 1432 1433 1434 1435 1436 1437 1438 1439 1440 1442 1443 1444
[325] 1445 1446 1447 1450 1451 1452 1453 1454 1458 1478 1479 1480
[337] 1481 1482 1483 1484 1490 1515 2137 3485 3487 3488 3489 3490
[349] 3491 3492 3495 3496 3497 3498 3499 3500 3501 3502 3503 3508
[361] 3509 3513 3518 3519 5312 5313 5317 5318 5319 5320 5321 5322
[373] 5323 5325 5326 5327 5328 5329 5330 5331 5332 5334 5335 5336
[385] 5337 5338 5339 5340 5341 5342 5343 5344 5345 5346 5347 5348
[397] 5349 5350 5368 5474 5475 5485 5486 5487 5488 5489 5491 5492
[409] 5509 5756 7545 8672 8673 8674 8675 8676 8677 8678 8679 8680
[421] 8681 8682 8683 8713 8714 8715 8716 8717 8718 8719 8720 8721
[433] 8722 8723 8724 8725 8726 8727 8728 8729 8730 8731 8732 8733
[445] 8734 8735 8736 8737 8738 8739 8740 8741 8742 8743 8744 8745
[457] 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755 8756 8757
[469] 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769
[481] 8770 8771 8772 8773 8774 8775 8776 8835 8836 8837 8839 8840
[493] 8841 8842 8843 8844 8845 8846 8847 8848 8849 8855 8856 8857
[505] 8858 8859 8862 8863 8864 8865 8876 8927 8932 8933 8945 10394
[517] 10395 10396 10397 10398 10399 10400 10401 10402 10403 10404 10405 10406
[529] 10407 10408 10410 10411 10413 10414 10421 10422 10425 10427 10428 10429
[541] 10430 10431 10432 10433 10434 10435 10436 10437 10438 10441 10442 10443
[553] 10444 10445 10446 10450 10538 10539 10540 10541 10544 10545 10546 10547
[565] 10552 10553 10554 10555 10556 10557 10558 10559 10570 10644 10647 10652
[577] 10656 10657 10659 10664 10665 10666 10667 10668 10669 10670 10671 10672
[589] 10673 10674 10675 10676 10677 10678 10679 10680 10681 10682 10683 10684
[601] 10685 10686 10687 10688 10689 10690 10691 10692 10693 10694 10695 10696
[613] 10697 10698 10699 10700 10701 10702 10703 10704 10705 10706 10707 10708
[625] 10709 10710 10711 10712 10713 10714 10715 10716 10717 10718 10719 10720
[637] 10721 10722 10723 10724 10725 10726 10727 10728 10729 10730 10731 10732
[649] 10733 10734 10735 10736 10737 10738 10739 10740 10741 10742 10743 10744
[661] 10745 10746 10747 10748 10749 10750 10751 10752 10753 10754 10755 10756
[673] 10757 10758 10759 10760 10761 10762 10763 10764 10765 10766 10767 10768
[685] 10769 10770 10771 10772 10773 10774 10775 10776 10777 10778 10779 10780
[697] 10781 10782 10783 10784 10785 10786 10787 10788 10789 10790 10791 10792
[709] 10793 10794 10795 10796 10797 10798 10799 10800 10801 10802 10803 10804
[721] 10805 10806 10807 10808 10809 10810 10811 10812 10813 10814 10815 10816
[733] 10817 10818 10819 10820 10830 10831 10832 10833 10919 10920 10921 10922
[745] 10923 10924 10925 10926 10927 10928 10929 10930 10931 10932 10933 10934
[757] 10935 10936 10937 10938 10939 10940 10941 10942 10943 10944 10945 10946
[769] 10947 10948 10949 10950 10951 10952 10953 10954 10955 10956 10957 10958
[781] 10959 10960 10964 10965 10966 10967 10968 10969 10970 10971 10972 10973
[793] 10974 10975 10976 10977 10978 10979 10980 10981 10982 10983 10984 10985
[805] 10986 10987 10988 10989 10990 10991 10992 10993 10994 10995 10997 10998
[817] 11000 11001 11002 11003 11004 11005 11006 11007 11008 11009 11010 11011
[829] 11012 11019 11020 11021 11022 11023 11024 11025 11026 11027 11028 11030
[841] 11031 11032 11033 11065 11077 11086 11093 11094 11095 11096 11097 11098
[853] 11099 11100 11101 11102 11103 11104 11105 11106 11107 11108 11109 11110
[865] 11111 11112 11113 11114 11115 11116 11117 11118 11119 11120 11122 11123
[877] 11124 11125 11126 11127 11128 11129 11130 11131 11132 11133 11134 11135
[889] 11136 11139 11140 11149 11150 11151 11204 11205 11210 11211 11212 11213
[901] 11214 11215 11216 11217 11218 11219 11220 11221 11222 11223 11224 11225
[913] 11226 11227 11228 11229 11230 11231 11232 11233 11234 11235 11236 11237
[925] 11238 11239 11240 11241 11242 11243 11244 11245 11246 11247 11248 11249
[937] 11250 11251 11252 11253 11254 11255 11256 11257 11258 11259 11260 11261
[949] 11262 11263 11264 11265 11266 11267 11268 11269 11270 11271 11272 11273
[961] 11274 11275 11276 11277 11278 11279 11280 11281 11282 11283 11284 11285
[973] 11286 11287 11288 11289 11290 11291 11292 11293 11294 11295 11296 11297
[985] 11298 11299 11300 11301 11302 11303 11304 11305 11306 11307 11308 11309
[997] 11310 11311 11312 11313
>>>>>>> Stashed changes
[ reached getOption("max.print") -- omitted 2851 entries ]
poss.storms35 <- hilomodified[index.lt25,]
poss.storms35
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
hilomodified$date[16]
[1] "2013-01-01 15:00:00 UTC"
storm.1.7.13 <- ggplot(hilomodified[145:168,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
<<<<<<< Updated upstream

RiverFlow1 <- ggplot(hilomodified[1:100,], aes(x = date, y = as.numeric(cms))) +
geom_line()
print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
getwd()
[1] "/Users/odalysbar/Documents/MA321-SP21/HiloBay-Adolf/HiloBay_MA321_Spring21"
# write.csv(hilomodified,file="HiloBayNEW13to15.csv", row.names = FALSE)
storms
[1] 2 3 4 5 6 8 9 10 11 13 14
[12] 15 16 17 18 19 20 22 24 25 26 28
[23] 29 33 34 35 37 38 40 41 44 45 46
[34] 47 48 49 50 52 55 57 58 59 60 63
[45] 64 65 66 67 70 71 72 73 74 75 78
[56] 79 80 86 87 88 89 91 92 93 94 95
[67] 96 97 98 99 100 102 103 104 105 106 107
[78] 108 109 110 111 112 113 114 115 116 117 118
[89] 119 120 121 122 123 124 125 126 127 128 129
[100] 130 131 132 133 134 135 136 137 138 139 140
[111] 141 142 143 144 145 146 147 148 149 150 151
[122] 152 153 154 155 156 157 158 159 160 161 162
[133] 163 164 165 166 167 168 169 170 171 172 173
[144] 174 175 176 177 178 179 180 181 182 184 185
[155] 186 187 188 189 190 191 192 193 194 195 196
[166] 197 198 199 200 201 202 203 204 205 206 207
[177] 208 209 210 211 212 213 214 215 216 217 218
[188] 219 220 221 222 223 224 225 226 227 228 229
[199] 230 231 232 233 234 235 236 237 238 239 240
[210] 241 242 243 244 245 246 247 248 249 250 251
[221] 252 253 254 255 256 257 258 259 260 261 262
[232] 263 264 265 266 267 268 269 270 271 272 273
[243] 274 275 276 277 278 279 280 281 282 283 284
[254] 285 286 287 288 289 290 291 292 293 294 295
[265] 296 297 298 299 300 301 302 303 304 305 306
[276] 307 308 309 310 311 312 313 314 315 316 317
[287] 318 319 320 321 322 323 324 325 326 327 328
[298] 330 331 332 333 334 335 336 337 338 339 340
[309] 342 344 345 346 347 348 349 350 351 354 355
[320] 356 357 358 359 360 361 362 364 365 366 368
[331] 369 370 371 373 374 375 376 377 378 379 381
[342] 383 384 385 386 387 388 389 390 392 394 396
[353] 397 398 399 400 401 402 404 407 408 410 411
[364] 412 416 417 418 420 421 426 427 428 429 430
[375] 431 432 433 434 437 438 439 440 441 442 443
[386] 444 445 446 447 448 449 450 451 452 454 455
[397] 456 457 459 464 465 466 472 473 474 475 476
[408] 477 482 483 484 490 492 495 496 497 503 504
[419] 505 513 514 517 523 524 532 534 535 538 543
[430] 544 549 550 551 552 554 555 557 559 560 568
[441] 571 572 574 575 577 578 579 580 581 582 583
[452] 585 586 590 591 592 593 594 595 596 599 600
[463] 603 604 605 612 613 620 621 622 635 636 637
[474] 638 639 645 646 648 649 650 651 652 657 658
[485] 660 661 662 663 664 665 666 667 670 671 672
[496] 689 690 691 692 693 694 695 696 697 698 699
[507] 700 701 702 703 704 706 707 710 711 712 713
[518] 714 715 716 724 725 727 729 730 731 741 742
[529] 747 748 749 750 751 752 753 754 755 756 757
[540] 758 759 760 761 762 763 764 765 766 767 768
[551] 769 770 771 772 773 775 776 779 780 784 785
[562] 790 791 792 802 804 805 806 807 815 817 818
[573] 819 829 831 832 833 834 835 836 838 839 840
[584] 841 842 844 845 846 847 848 849 850 851 852
[595] 853 854 855 856 857 858 859 861 863 864 865
[606] 866 868 872 873 874 875 876 877 878 879 881
[617] 882 883 884 885 886 887 888 889 890 891 892
[628] 893 894 895 896 897 898 899 900 901 902 903
[639] 904 905 906 907 908 909 910 911 912 913 914
[650] 915 916 917 918 919 920 921 922 923 924 925
[661] 926 927 928 929 930 931 932 933 934 935 936
[672] 937 938 939 940 941 942 943 944 945 946 947
[683] 948 949 950 951 952 953 954 955 956 957 958
[694] 959 960 961 962 963 964 965 966 967 968 969
[705] 970 971 972 973 974 975 976 977 978 979 980
[716] 981 982 983 984 985 986 987 988 989 990 991
[727] 992 993 994 995 996 997 998 999 1000 1001 1002
[738] 1003 1004 1006 1007 1008 1009 1010 1011 1012 1013 1014
[749] 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026
[760] 1027 1028 1029 1030 1032 1033 1034 1035 1036 1038 1039
[771] 1040 1041 1042 1043 1044 1046 1047 1048 1050 1052 1053
[782] 1054 1055 1056 1058 1061 1062 1063 1067 1068 1069 1070
[793] 1071 1074 1076 1077 1078 1079 1080 1082 1083 1084 1085
[804] 1086 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099
[815] 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
[826] 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121
[837] 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132
[848] 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143
[859] 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154
[870] 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165
[881] 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176
[892] 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187
[903] 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198
[914] 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209
[925] 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
[936] 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
[947] 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242
[958] 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253
[969] 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
[980] 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275
[991] 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285
[ reached getOption("max.print") -- omitted 17039 entries ]
vector <- vector()
for(i in 1:length(storms)){
if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
stop <- storms[i]
}else{
stop <- 0
}
if(stop > 0){
vector[i] <- stop
}
}
vector[which(is.na(vector)==FALSE)]
[1] 622 672 1706 1898 1924 1970 2081 2327 2388 2429
[11] 2469 2493 2515 2537 2576 2714 2734 2772 2799 2825
[21] 2866 2947 2998 3051 3068 3130 3196 3211 3226 3249
[31] 3373 3746 3841 3856 3898 4343 4412 4438 4460 4507
[41] 4534 4617 4660 4827 5006 5087 5107 5135 5237 5667
[51] 5690 5735 5767 5786 5837 5851 5888 5900 5921 5968
[61] 6048 6068 6092 6240 6270 6284 6320 6363 6379 6456
[71] 6488 6599 6630 6864 6888 6908 6945 6959 6977 6999
[81] 7025 7063 7076 7099 7134 7150 7222 7308 7443 7466
[91] 7493 7678 7703 7733 7786 7805 7840 7863 7882 7933
[101] 7960 7978 8005 8031 8124 8167 8954 9013 9067 9091
[111] 9135 9160 9188 9256 9280 9303 9333 9377 9487 9505
[121] 10205 12119 12216 12238 13083 13095 14759 14786 14812 14831
[131] 14891 14946 14998 15028 15079 15099 15125 15140 15165 15187
[141] 15209 15526 15540 15553 15702 17365 17482 17512 17533 17585
[151] 17612 17657 17708 17734 17778 17811 17838 17871 17925 17978
[161] 18210 18233 18358 18888 18905 18921 19117 19192 19225 19269
[171] 19322 19342 19362 19378 19490 19504 19522 19628 21792 21813
[181] 21948 21981 22016 22075 22124 22220 23568
storm.1.7.13 <- ggplot(hilomodified[1:80,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
Error in ggplot(hilomodified[1:80, ], aes(x = date, y = as.numeric(cms))) :
could not find function "ggplot"
startaxis <- vector()
endaxis <- vector()
i <- 1
while (i <= length(change.vector)) {
if(isTRUE(change.vector[i] < 0)){
startaxis[i] <- RiverFlow[i]
}
i <- i+1
}
begin<-which(is.na(startaxis) == FALSE)
begin
[1] 33 58 77 85 86 87 88 90 91 92 93 94 95 96 103 104 105 106 108
[20] 109 113 114 115 116 117 118 126 127 128 136 137 138 139 140 141 142 143 144
[39] 145 146 147 148 149 150 151 152 153 188 190 191 192 193 194 195 196 197 198
[58] 201 202 210 211 212 213 214 215 286 287 290 291 292 293 294 295 346 348 373
[77] 376 383 396 416 432 438 439 542 550 553 556 558 559 574 577 578 579 580 581
[96] 582 584 585 635 637 645 650 656 657 688 689 690 691 712 713 728 746 747 748
[115] 749 750 751 752 753 754 804 816 817 818 830 832 833 838 840 841 843 844 845
[134] 846 847 848 849 850 851 852 853 854 865 881 882 883 884 885 886 890 891 892
[153] 893 894 905 950 951 953 954 955 956 957 958 959 960 961 962 963 985 1012 1016
[172] 1055 1076 1077 1078 1084 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1104 1105 1106
[191] 1107 1108 1109 1110 1123 1124 1140 1141 1142 1143 1144 1145 1146 1147 1148 1150 1158 1159 1160
[210] 1161 1162 1163 1165 1182 1196 1197 1198 1199 1204 1205 1206 1207 1208 1209 1210 1211 1241 1242
[229] 1243 1244 1245 1258 1260 1261 1262 1278 1279 1280 1281 1282 1283 1284 1290 1291 1292 1293 1294
[248] 1295 1296 1304 1305 1313 1314 1318 1333 1336 1337 1338 1339 1340 1341 1368 1370 1371 1372 1373
[267] 1374 1432 1462 1466 1472 1513 1526 1543 1550 1552 1554 1559 1587 1590 1591 1613 1674 1676 1683
[286] 1687 1695 1717 1718 1719 1720 1721 1722 1742 1743 1744 1783 1789 1807 1808 1838 1840 1849 1862
[305] 1884 1887 1895 1919 1939 1940 1957 1968 1985 1986 1988 1989 1990 1992 1994 1995 1996 2005 2006
[324] 2007 2008 2009 2010 2011 2013 2014 2015 2016 2017 2018 2023 2024 2058 2061 2063 2094 2101 2102
[343] 2103 2104 2105 2106 2107 2108 2109 2110 2111 2126 2127 2128 2129 2198 2212 2239 2240 2241 2270
[362] 2272 2274 2296 2309 2311 2325 2352 2353 2354 2355 2358 2359 2382 2417 2418 2452 2455 2456 2457
[381] 2551 2552 2553 2554 2571 2574 2590 2591 2592 2595 2596 2597 2604 2605 2606 2607 2608 2609 2610
[400] 2611 2612 2647 2659 2660 2661 2662 2669 2670 2671 2760 2767 2768 2770 2783 2787 2796 2843 2844
[419] 2862 2864 2879 2881 2886 2887 2890 2891 2892 2901 2902 2903 2904 2905 2906 2907 2909 2910 2918
[438] 2919 2971 2972 2977 3044 3045 3127 3140 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3156
[457] 3157 3158 3174 3194 3224 3225 3237 3238 3239 3240 3241 3242 3265 3266 3280 3281 3282 3283 3294
[476] 3295 3296 3302 3303 3328 3329 3330 3331 3338 3367 3387 3392 3397 3398 3399 3400 3401 3428 3430
[495] 3432 3437 3454 3457 3458 3459 3460 3461 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481
[514] 3482 3483 3484 3498 3499 3507 3509 3513 3518 3519 3520 3521 3522 3523 3544 3545 3546 3547 3548
[533] 3549 3611 3619 3660 3732 3742 3744 3766 3767 3773 3774 3775 3790 3799 3815 3817 3819 3820 3853
[552] 3869 3883 3884 3893 3895 3912 3913 3914 3915 3916 3917 3920 3941 3942 3943 3944 3945 3946 3947
[571] 3948 3949 3950 3951 3990 3991 3992 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4032 4033
[590] 4034 4041 4042 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4099 4111 4116 4120 4121 4122
[609] 4123 4124 4125 4126 4127 4128 4129 4152 4156 4161 4163 4173 4174 4180 4206 4207 4224 4225 4234
[628] 4242 4247 4248 4249 4250 4251 4252 4253 4254 4255 4303 4312 4313 4314 4315 4366 4376 4381 4382
[647] 4383 4384 4389 4391 4393 4422 4423 4424 4458 4459 4486 4487 4488 4567 4571 4585 4586 4591 4592
[666] 4593 4594 4595 4596 4608 4610 4613 4614 4615 4616 4639 4640 4641 4677 4678 4679 4680 4690 4693
[685] 4701 4703 4704 4705 4707 4729 4730 4731 4732 4734 4735 4736 4737 4749 4750 4767 4768 4773 4774
[704] 4775 4776 4777 4778 4779 4780 4781 4782 4783 4785 4786 4787 4788 4848 4849 4850 4851 4852 4853
[723] 4854 4855 4862 4863 4864 4865 4883 4884 4885 4886 4887 4888 4889 4890 4891 4943 4944 4956 4972
[742] 4987 4999 5000 5035 5042 5043 5044 5045 5047 5051 5070 5071 5072 5099 5123 5132 5133 5134 5157
[761] 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167 5168 5169 5194 5196 5197 5198 5208 5209 5211
[780] 5212 5213 5214 5253 5254 5258 5279 5280 5292 5293 5298 5300 5301 5302 5303 5304 5305 5307 5308
[799] 5309 5310 5311 5312 5313 5319 5320 5388 5389 5390 5400 5401 5403 5404 5405 5430 5431 5433 5454
[818] 5455 5456 5457 5458 5459 5460 5461 5462 5463 5464 5468 5469 5470 5471 5472 5474 5536 5538 5546
[837] 5547 5548 5549 5550 5551 5552 5587 5589 5595 5596 5597 5598 5599 5600 5620 5665 5677 5678 5679
[856] 5713 5718 5719 5720 5721 5762 5763 5784 5815 5873 5884 5885 5917 5954 5955 5956 5957 5958 5959
[875] 5960 5994 5995 5996 5999 6000 6001 6002 6003 6004 6005 6006 6008 6046 6065 6126 6127 6128 6129
[894] 6150 6157 6159 6160 6161 6162 6175 6189 6190 6197 6199 6219 6220 6221 6222 6223 6265 6266 6269
[913] 6319 6358 6359 6362 6376 6435 6437 6439 6440 6453 6454 6455 6482 6483 6484 6485 6486 6487 6509
[932] 6510 6511 6513 6514 6515 6516 6527 6528 6529 6530 6531 6532 6533 6534 6535 6545 6546 6547 6548
[951] 6549 6550 6551 6552 6553 6554 6589 6590 6591 6598 6622 6623 6624 6625 6626 6628 6629 6654 6656
[970] 6657 6662 6663 6664 6665 6667 6668 6669 6670 6671 6672 6673 6674 6677 6679 6680 6710 6711 6726
[989] 6727 6728 6729 6740 6742 6743 6744 6745 6746 6747 6748 6749
[ reached getOption("max.print") -- omitted 3439 entries ]
while (i <= length(change.vector)) {
if(isTRUE(change.vector[i] > 0)){
endaxis[i] <- RiverFlow[i]
}
i <- i+1
}
end <- which(is.na(endaxis)==FALSE)
storm.1.7.13 <- ggplot(hilomodified[begin,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
storm.1.7.13 <- ggplot(hilomodified[end,], aes(x = date, y = as.numeric(cms))) +
geom_line(color="black")+
geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")
print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
while (i <= length(change.vector)) {
if(isTRUE(change.vector[i] < 0)){
startaxis[i] <- RiverFlow[i]
i <- i+1
}else{
if(isTRUE(change.vector[i] > 0)){
endaxis[i] <- RiverFlow[i]
}
}
}